I am a fan of anchor modelling and temporal databases. I find powerful the idea that the database's current dataset includes all previous datasets, and that the current schema version includes all previous schema versions. One organization should be able to obtain today a report with the same results already obtained in the past, even if the underlaying dataset has been corrected, completed, or has evolved in any way. A data warehouse should be a temporal database, allowing to provide information both in its current state and in the past states (when it was still wrong or incomplete), as well as provide details on when and how was later corrected, completed or even deleted. And the same concept should apply to a datalake, whatever its implementation. Rather tan "immutable", it should be updatable in a non-destructive way.